17 - LV Genomic Variant Test
Sara Gosline
2019-11-08
Get Data
This is nearly entirely based on the code in notebook 09 and that in 11.
We have latent variable expression analysis data - Latent Variable Feather File
For this data we are also using any data for which there are gene variants (cNFs, pNFs, MPNSTs): - Exome-Seq variants - WGS Variants
Let’s see if there are any LVs that split based on gene variant. Because we’re having trouble scaling with the number of latent variables, I only look at variants that occur in less than 5% of the population. notice this is a difference from notebook #11.
wgs.vars=synTableQuery("SELECT Hugo_Symbol,Protein_position,specimenID,IMPACT,FILTER,ExAC_AF FROM syn20551862")$asDataFrame()##
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Downloading [####################]100.00% 76.0MB/76.0MB (9.5MB/s) Job-99615053795621000143736649.csv Done...all.vars<-rbind(select(wgs.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','ExAC_AF'),
select(exome.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','ExAC_AF'))%>%
subset(ExAC_AF<0.05)
fn <- tempfile(pattern = "", fileext = ".feather")
download.file('https://github.com/Sage-Bionetworks/nf-lv-viz/raw/master/data/filt_nf_mp_res.feather', destfile = fn)
mp_res<-read_feather(fn) %>% filter(sex != "NA", isCellLine != "TRUE")%>%
select(latent_var,id,value,sd_value,specimenID,tumorType,modelOf,diagnosis)Merge data together
For the purposes of this analysis we want to have only those samples wtih genomic data.
samps<-intersect(mp_res$specimenID,all.vars$specimenID)
print(samps)## [1] "2-019 Neurofibroma"
## [2] "2-021 Neurofibroma"
## [3] "2-001 Plexiform Neurofibroma"
## [4] "2-012 Neurofibroma"
## [5] "2-029 Neurofibroma"
## [6] "2-025 Neurofibroma"
## [7] "2-014 Neurofibroma"
## [8] "2-032 Plexiform Neurofibroma"
## [9] "2-004 Plexiform Neurofibroma"
## [10] "2-013 Plexiform Neurofibroma"
## [11] "2-031 Malignant Peripheral Nerve Sheath Tumor"
## [12] "2-010 Neurofibroma"
## [13] "2-026 Neurofibroma"
## [14] "2-016 Neurofibroma"
## [15] "2-005 Neurofibroma"
## [16] "2-017 Neurofibroma"
## [17] "2-006 Plexiform Neurofibroma"
## [18] "patient10tumor1"
## [19] "patient10tumor2"
## [20] "patient10tumor3"
## [21] "patient11tumor1"
## [22] "patient13tumor3"
## [23] "patient2tumor1"
## [24] "patient3tumor1"
## [25] "patient3tumor2"
## [26] "patient3tumor4"
## [27] "patient4tumor1"
## [28] "patient4tumor10"
## [29] "patient4tumor4"
## [30] "patient4tumor9"
## [31] "patient5tumor5"
## [32] "patient6tumor4"
## [33] "patient6tumor5"
## [34] "patient6tumor6"
## [35] "patient6tumor7"
## [36] "patient8tumor4"
## [37] "patient8tumor5"
## [38] "patient8tumor6"
## [39] "patient9tumor1"
## [40] "patient9tumor6"Retrieve Variant Data
Let’s retrieve the LV data and evaluate any correlations between scores and tumor size or patient age
data.with.var<-mp_res%>%subset(specimenID%in%samps)%>%
left_join(all.vars,by='specimenID')
tab<-subset(data.with.var,!tumorType%in%c('Other','High Grade Glioma','Low Grade Glioma'))
top.genes=tab%>%group_by(tumorType)%>%
mutate(numSamps=n_distinct(specimenID))%>%
group_by(tumorType,Hugo_Symbol)%>%
mutate(numMutated=n_distinct(specimenID))%>%
ungroup()%>%
subset(numMutated>2)%>%
subset(numMutated<(numSamps-1))%>%
select(tumorType,Hugo_Symbol,numSamps,numMutated)%>%distinct()
gene.count=top.genes%>%group_by(tumorType)%>%mutate(numGenes=n_distinct(Hugo_Symbol))%>%select(tumorType,numGenes)%>%distinct()
DT::datatable(gene.count)Now we can loop through every tumor type and gene
red.genes<-c("NF1","SUZ12","CDKN2A","EED")##for testing
vals<-tab%>%#subset(Hugo_Symbol%in%red.genes)%>%
mutate(mutated=ifelse(is.na(IMPACT),'WT','Mutated'))%>%
select(latent_var,tumorType,value,Hugo_Symbol,specimenID,mutated)%>%
distinct()%>%
spread(key=Hugo_Symbol,value='mutated',fill='WT')
counts<-vals%>%
gather(key=gene,value=status,-c(latent_var,tumorType,value,specimenID))%>%
select(latent_var,tumorType,value,gene,specimenID,status)%>%
group_by(latent_var,tumorType,gene)%>%
mutate(numVals=n_distinct(status))%>%
subset(numVals==2)%>%ungroup()
#so now we have only
with.sig<-counts%>%ungroup()%>%subset(gene%in%top.genes$Hugo_Symbol)%>%
group_by(latent_var,gene)%>%
mutate(pval=t.test(value~status)$p.value)%>%ungroup()%>%
group_by(latent_var)%>%
mutate(corP=p.adjust(pval))%>%ungroup()%>%
select(latent_var,gene,pval,corP)%>%distinct()
sig.vals<-subset(with.sig,corP<0.05)
DT::datatable(sig.vals)Interesting! Some genes actually pass p-value correction. What do they look like? Here let’s write the messiest possible code to print.
for(ct in unique(sig.vals$latent_var)){
tplot<-sig.vals[which(sig.vals$latent_var==ct),]
if(nrow(tplot)==0)
next
print(tplot)
p<-counts%>%
subset(latent_var==ct)%>%
subset(gene%in%tplot$gene)%>%
ggplot(aes(x=gene,y=value,col=status))+
geom_boxplot(outlier.shape=NA)+
geom_point(position=position_jitterdodge(),aes(group=status))+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle(paste(ct,'scores'))
# if(method=='cibersort')
# p<-p+scale_y_log10()
print(p)
}## # A tibble: 104 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AADACL4 1.12e-10 0.0000331
## 2 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AC018445.1 1.12e-10 0.0000331
## 3 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AL645922.1 1.12e-10 0.0000331
## 4 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ANKRD34B 2.63e-12 0.000000781
## 5 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ANKRD65 9.04e-11 0.0000268
## 6 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AP3S1 1.12e-10 0.0000331
## 7 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE APEX1 9.04e-11 0.0000268
## 8 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AQP1 1.12e-10 0.0000331
## 9 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ARAF 1.12e-10 0.0000331
## 10 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ARFGEF2 1.12e-10 0.0000331
## # … with 94 more rows
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 828 AL109659.1 0.00000000200 0.000593
## 2 LV 828 IGKV6D-21 0.000000153 0.0456
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 830 AL109659.1 0.0000000483 0.0144
## # A tibble: 16 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 984 AL109659.1 0.00000000532 0.00158
## 2 LV 984 BCL2L13 0.0000000621 0.0184
## 3 LV 984 CPEB2 0.0000000262 0.00777
## 4 LV 984 ESRRA 0.0000000637 0.0189
## 5 LV 984 GLRA1 0.0000000978 0.0290
## 6 LV 984 HOXD8 0.0000000621 0.0184
## 7 LV 984 LILRA4 0.0000000978 0.0290
## 8 LV 984 MOCOS 0.0000000621 0.0184
## 9 LV 984 NUDT19 0.0000000978 0.0290
## 10 LV 984 OR4C5 0.00000000658 0.00195
## 11 LV 984 OR8B2 0.0000000621 0.0184
## 12 LV 984 PDIA6 0.0000000978 0.0290
## 13 LV 984 SIMC1 0.0000000621 0.0184
## 14 LV 984 WDR92 0.0000000621 0.0184
## 15 LV 984 ZDHHC13 0.0000000621 0.0184
## 16 LV 984 ZNF395 0.000000110 0.0325
## # A tibble: 4 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 24,PID_DELTANP63PATHWAY APC 0.00000000149 0.000444
## 2 24,PID_DELTANP63PATHWAY KRT18 0.000000168 0.0498
## 3 24,PID_DELTANP63PATHWAY PLOD3 0.00000000415 0.00123
## 4 24,PID_DELTANP63PATHWAY PRH2 0.00000000415 0.00123
## # A tibble: 8 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 728,SVM Mast cells activated ASB8 0.0000000924 0.0274
## 2 728,SVM Mast cells activated BAG5 0.0000000924 0.0274
## 3 728,SVM Mast cells activated C14orf166B 0.0000000924 0.0274
## 4 728,SVM Mast cells activated COL2A1 0.0000000924 0.0274
## 5 728,SVM Mast cells activated GAK 0.0000000885 0.0263
## 6 728,SVM Mast cells activated JUP 0.000000161 0.0478
## 7 728,SVM Mast cells activated PGPEP1L 0.0000000924 0.0274
## 8 728,SVM Mast cells activated SNX1 0.0000000621 0.0184
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 671,REACTOME_COLLAGEN_FORMATION C20orf196 0.0000000224 0.00665
## 2 671,REACTOME_COLLAGEN_FORMATION EID2 0.000000118 0.0350
## 3 671,REACTOME_COLLAGEN_FORMATION FAM210A 0.0000000513 0.0152
## # A tibble: 8 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 161,REACTOME_TRNA_AMINOACYLATION CLCC1 0.0000000760 0.0226
## 2 161,REACTOME_TRNA_AMINOACYLATION DGCR8 0.0000000658 0.0195
## 3 161,REACTOME_TRNA_AMINOACYLATION OR2A7 0.0000000616 0.0183
## 4 161,REACTOME_TRNA_AMINOACYLATION POU5F1 0.0000000582 0.0173
## 5 161,REACTOME_TRNA_AMINOACYLATION PTPRS 0.0000000432 0.0128
## 6 161,REACTOME_TRNA_AMINOACYLATION SEMA3D 0.000000134 0.0398
## 7 161,REACTOME_TRNA_AMINOACYLATION TSNAXIP1 0.0000000197 0.00585
## 8 161,REACTOME_TRNA_AMINOACYLATION UBTD2 0.0000000866 0.0257
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 822 ESRRA 0.000000134 0.0397
## 2 LV 822 OR4C5 0.0000000161 0.00477
## # A tibble: 4 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 1,REACTOME_MRNA_SPLICING HLA-C 0.00000000708 0.00210
## 2 1,REACTOME_MRNA_SPLICING LILRA6 0.000000121 0.0359
## 3 1,REACTOME_MRNA_SPLICING PLOD3 0.0000000642 0.0191
## 4 1,REACTOME_MRNA_SPLICING PRH2 0.0000000642 0.0191
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 397 HLA-C 1.52e- 7 0.0452
## 2 LV 397 IGHV7-81 2.98e-11 0.00000886
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 84,KEGG_SPLICEOSOME IGHV3-11 0.0000000435 0.0129
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 82,PID_RAC1_PATHWAY IGHV7-81 6.93e- 8 0.0206
## 2 82,PID_RAC1_PATHWAY KRT18 1.79e-10 0.0000532
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 881,REACTOME_DNA_STRAND_ELONGATION IRX6 1.14e-10 0.0000339
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 310,REACTOME_FORMATION_OF_THE_TERNARY_COMPLEX_AN… LONP1 3.99e-8 0.0119
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 71 NBPF12 0.0000000453 0.0134
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 53 NPIPB11 0.00000000562 0.00167
## 2 LV 53 TAS2R46 0.000000100 0.0297
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 835 OR4C5 0.0000000884 0.0263
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 60 PER3 0.000000144 0.0428
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 39,SVM Dendritic cells resting PLOD3 0.00000000108 0.000321
## 2 39,SVM Dendritic cells resting PRH2 0.00000000108 0.000321
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 962 PLOD3 0.00000000101 0.000299
## 2 LV 962 PRH2 0.00000000101 0.000299
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 116,REACTOME_INTERFERON_ALPHA_BETA_SIG… RP11-1396O13… 2.45e-8 0.00727
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 509 RP11-1396O13.13 3.51e-12 0.00000104
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 904 RP11-1396O13.13 0.0000000374 0.0111
#}Breaking down by tumor type
At first glance it seems that a lot of these are separating out cNFs (i.e. mast cell signaling) from other types. However, I’m getting the same error I get in notebook number 11, so am unsure about how to proceed.